Blog entries

From farm to fork, via AI - a case study for distinguishing different grain cultivars

In their ongoing work, QuoData's scientists are pleased to share some of the recent developments in their latest paper - "AI-based identification of grain cultivars via non-target mass spectrometry". The paper deals with the use of non-target high-resolution mass spectrometry and the processing of the extensive data using artificial intelligence approaches.   


From farm to fork, via artificial intelligence

The range of human activity directly or indirectly involved in addressing issues related to food safety has considerably expanded in the last decade. In the past, the focus in food safety lay on quality control measures implemented to ensure productive processes met performance criteria. Nowadays, however, a narrow understanding of the issues surrounding food safety will compromise our ability to fully mobilize the potential of new digital and AI technologies.

Evaluating dependability of black box AI methods

Artificial intelligence methods are now seeping into healthcare, medicine, agriculture and food sectors. Keeping the buzz aside, such methods are increasingly efficient at addressing a task at hand. The area of food safety is not unknown to these technological developments in big data and contemporary machine learning methods. For example, use of AI as part of analytical measurement procedures, internet of things (IoT) enabled analytical devices, use of predictive analytics algorithms for crunching large data sets, to list a few.

Algorithm A

ISO standards and their stories

The Untold History of Algorithm A

Bertrand Colson

QuoData, Germany 


I recently faced the problem of trying to understand why different people applying the same algorithm to the same data obtained different results, even though everyone was apparently applying the algorithm correctly.


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